Overview

Dataset statistics

Number of variables14
Number of observations8886058
Missing cells35271795
Missing cells (%)28.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory949.1 MiB
Average record size in memory112.0 B

Variable types

Numeric6
Categorical8

Alerts

store_id has a high cardinality: 63 distinct valuesHigh cardinality
product_id has a high cardinality: 615 distinct valuesHigh cardinality
date has a high cardinality: 1033 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with store_idHigh correlation
sales is highly overall correlated with revenue and 3 other fieldsHigh correlation
revenue is highly overall correlated with sales and 2 other fieldsHigh correlation
stock is highly overall correlated with promo_bin_2 and 1 other fieldsHigh correlation
promo_discount_2 is highly overall correlated with promo_bin_1 and 3 other fieldsHigh correlation
store_id is highly overall correlated with Unnamed: 0High correlation
promo_bin_1 is highly overall correlated with sales and 3 other fieldsHigh correlation
promo_type_2 is highly overall correlated with promo_discount_2 and 2 other fieldsHigh correlation
promo_bin_2 is highly overall correlated with sales and 6 other fieldsHigh correlation
promo_discount_type_2 is highly overall correlated with sales and 6 other fieldsHigh correlation
promo_type_1 is highly imbalanced (77.3%)Imbalance
promo_type_2 is highly imbalanced (99.1%)Imbalance
sales has 302296 (3.4%) missing valuesMissing
revenue has 302296 (3.4%) missing valuesMissing
stock has 302296 (3.4%) missing valuesMissing
price has 91381 (1.0%) missing valuesMissing
promo_bin_1 has 7653515 (86.1%) missing valuesMissing
promo_bin_2 has 8873337 (99.9%) missing valuesMissing
promo_discount_2 has 8873337 (99.9%) missing valuesMissing
promo_discount_type_2 has 8873337 (99.9%) missing valuesMissing
sales is highly skewed (γ1 = 1557.844936)Skewed
revenue is highly skewed (γ1 = 815.4548181)Skewed
stock is highly skewed (γ1 = 24.21927272)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
sales has 7048907 (79.3%) zerosZeros
revenue has 7049979 (79.3%) zerosZeros

Reproduction

Analysis started2023-06-20 21:29:56.131666
Analysis finished2023-06-20 21:34:21.000703
Duration4 minutes and 24.87 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct8886058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4443029.5
Minimum1
Maximum8886058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.8 MiB
2023-06-20T22:34:21.204976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile444303.85
Q12221515.2
median4443029.5
Q36664543.8
95-th percentile8441755.2
Maximum8886058
Range8886057
Interquartile range (IQR)4443028.5

Descriptive statistics

Standard deviation2565184.1
Coefficient of variation (CV)0.57735024
Kurtosis-1.2
Mean4443029.5
Median Absolute Deviation (MAD)2221514.5
Skewness-1.3646306 × 10-15
Sum3.9481018 × 1013
Variance6.5801696 × 1012
MonotonicityStrictly increasing
2023-06-20T22:34:21.409839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
5924034 1
 
< 0.1%
5924048 1
 
< 0.1%
5924047 1
 
< 0.1%
5924046 1
 
< 0.1%
5924045 1
 
< 0.1%
5924044 1
 
< 0.1%
5924043 1
 
< 0.1%
5924042 1
 
< 0.1%
5924041 1
 
< 0.1%
Other values (8886048) 8886048
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
8886058 1
< 0.1%
8886057 1
< 0.1%
8886056 1
< 0.1%
8886055 1
< 0.1%
8886054 1
< 0.1%
8886053 1
< 0.1%
8886052 1
< 0.1%
8886051 1
< 0.1%
8886050 1
< 0.1%
8886049 1
< 0.1%

store_id
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.8 MiB
S0038
 
334082
S0085
 
325409
S0097
 
279019
S0094
 
276217
S0104
 
271338
Other values (58)
7399993 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters44430290
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS0002
2nd rowS0002
3rd rowS0002
4th rowS0002
5th rowS0002

Common Values

ValueCountFrequency (%)
S0038 334082
 
3.8%
S0085 325409
 
3.7%
S0097 279019
 
3.1%
S0094 276217
 
3.1%
S0104 271338
 
3.1%
S0062 267921
 
3.0%
S0026 266261
 
3.0%
S0056 260416
 
2.9%
S0020 253996
 
2.9%
S0108 249346
 
2.8%
Other values (53) 6102053
68.7%

Length

2023-06-20T22:34:21.624535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s0038 334082
 
3.8%
s0085 325409
 
3.7%
s0097 279019
 
3.1%
s0094 276217
 
3.1%
s0104 271338
 
3.1%
s0062 267921
 
3.0%
s0026 266261
 
3.0%
s0056 260416
 
2.9%
s0020 253996
 
2.9%
s0108 249346
 
2.8%
Other values (53) 6102053
68.7%

Most occurring characters

ValueCountFrequency (%)
0 17984981
40.5%
S 8886058
20.0%
1 3355402
 
7.6%
2 3330114
 
7.5%
5 2016937
 
4.5%
8 1722869
 
3.9%
6 1692194
 
3.8%
3 1655994
 
3.7%
4 1418789
 
3.2%
9 1281762
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35544232
80.0%
Uppercase Letter 8886058
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17984981
50.6%
1 3355402
 
9.4%
2 3330114
 
9.4%
5 2016937
 
5.7%
8 1722869
 
4.8%
6 1692194
 
4.8%
3 1655994
 
4.7%
4 1418789
 
4.0%
9 1281762
 
3.6%
7 1085190
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
S 8886058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35544232
80.0%
Latin 8886058
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17984981
50.6%
1 3355402
 
9.4%
2 3330114
 
9.4%
5 2016937
 
5.7%
8 1722869
 
4.8%
6 1692194
 
4.8%
3 1655994
 
4.7%
4 1418789
 
4.0%
9 1281762
 
3.6%
7 1085190
 
3.1%
Latin
ValueCountFrequency (%)
S 8886058
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44430290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17984981
40.5%
S 8886058
20.0%
1 3355402
 
7.6%
2 3330114
 
7.5%
5 2016937
 
4.5%
8 1722869
 
3.9%
6 1692194
 
3.8%
3 1655994
 
3.7%
4 1418789
 
3.2%
9 1281762
 
2.9%

product_id
Categorical

Distinct615
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.8 MiB
P0664
 
59051
P0125
 
58708
P0261
 
58504
P0364
 
58428
P0131
 
58117
Other values (610)
8593250 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters44430290
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowP0001
2nd rowP0005
3rd rowP0011
4th rowP0015
5th rowP0017

Common Values

ValueCountFrequency (%)
P0664 59051
 
0.7%
P0125 58708
 
0.7%
P0261 58504
 
0.7%
P0364 58428
 
0.7%
P0131 58117
 
0.7%
P0694 57956
 
0.7%
P0116 57940
 
0.7%
P0390 57872
 
0.7%
P0372 57699
 
0.6%
P0333 57473
 
0.6%
Other values (605) 8304310
93.5%

Length

2023-06-20T22:34:21.815333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p0664 59051
 
0.7%
p0125 58708
 
0.7%
p0261 58504
 
0.7%
p0364 58428
 
0.7%
p0131 58117
 
0.7%
p0694 57956
 
0.7%
p0116 57940
 
0.7%
p0390 57872
 
0.7%
p0372 57699
 
0.6%
p0333 57473
 
0.6%
Other values (605) 8304310
93.5%

Most occurring characters

ValueCountFrequency (%)
0 11721008
26.4%
P 8886058
20.0%
1 3416438
 
7.7%
6 3077549
 
6.9%
4 2994432
 
6.7%
5 2974769
 
6.7%
2 2926654
 
6.6%
3 2851268
 
6.4%
7 2273576
 
5.1%
9 1760049
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35544232
80.0%
Uppercase Letter 8886058
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11721008
33.0%
1 3416438
 
9.6%
6 3077549
 
8.7%
4 2994432
 
8.4%
5 2974769
 
8.4%
2 2926654
 
8.2%
3 2851268
 
8.0%
7 2273576
 
6.4%
9 1760049
 
5.0%
8 1548489
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
P 8886058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35544232
80.0%
Latin 8886058
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11721008
33.0%
1 3416438
 
9.6%
6 3077549
 
8.7%
4 2994432
 
8.4%
5 2974769
 
8.4%
2 2926654
 
8.2%
3 2851268
 
8.0%
7 2273576
 
6.4%
9 1760049
 
5.0%
8 1548489
 
4.4%
Latin
ValueCountFrequency (%)
P 8886058
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44430290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11721008
26.4%
P 8886058
20.0%
1 3416438
 
7.7%
6 3077549
 
6.9%
4 2994432
 
6.7%
5 2974769
 
6.7%
2 2926654
 
6.6%
3 2851268
 
6.4%
7 2273576
 
5.1%
9 1760049
 
4.0%

date
Categorical

Distinct1033
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.8 MiB
2019-08-10
 
10090
2019-08-17
 
10054
2019-06-15
 
10036
2019-06-22
 
10032
2019-07-13
 
10002
Other values (1028)
8835844 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters88860580
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-01-02
2nd row2017-01-02
3rd row2017-01-02
4th row2017-01-02
5th row2017-01-02

Common Values

ValueCountFrequency (%)
2019-08-10 10090
 
0.1%
2019-08-17 10054
 
0.1%
2019-06-15 10036
 
0.1%
2019-06-22 10032
 
0.1%
2019-07-13 10002
 
0.1%
2019-06-01 10001
 
0.1%
2019-08-12 10000
 
0.1%
2019-08-15 9999
 
0.1%
2019-08-09 9998
 
0.1%
2019-08-16 9996
 
0.1%
Other values (1023) 8785850
98.9%

Length

2023-06-20T22:34:21.992133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-08-10 10090
 
0.1%
2019-08-17 10054
 
0.1%
2019-06-15 10036
 
0.1%
2019-06-22 10032
 
0.1%
2019-07-13 10002
 
0.1%
2019-06-01 10001
 
0.1%
2019-08-12 10000
 
0.1%
2019-08-15 9999
 
0.1%
2019-08-09 9998
 
0.1%
2019-08-16 9996
 
0.1%
Other values (1023) 8785850
98.9%

Most occurring characters

ValueCountFrequency (%)
0 20191255
22.7%
- 17772116
20.0%
1 15980631
18.0%
2 13886421
15.6%
8 5008307
 
5.6%
9 4618374
 
5.2%
7 4325631
 
4.9%
3 2086079
 
2.3%
5 1677615
 
1.9%
6 1670183
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71088464
80.0%
Dash Punctuation 17772116
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20191255
28.4%
1 15980631
22.5%
2 13886421
19.5%
8 5008307
 
7.0%
9 4618374
 
6.5%
7 4325631
 
6.1%
3 2086079
 
2.9%
5 1677615
 
2.4%
6 1670183
 
2.3%
4 1643968
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 17772116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88860580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20191255
22.7%
- 17772116
20.0%
1 15980631
18.0%
2 13886421
15.6%
8 5008307
 
5.6%
9 4618374
 
5.2%
7 4325631
 
4.9%
3 2086079
 
2.3%
5 1677615
 
1.9%
6 1670183
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88860580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20191255
22.7%
- 17772116
20.0%
1 15980631
18.0%
2 13886421
15.6%
8 5008307
 
5.6%
9 4618374
 
5.2%
7 4325631
 
4.9%
3 2086079
 
2.3%
5 1677615
 
1.9%
6 1670183
 
1.9%

sales
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct5435
Distinct (%)0.1%
Missing302296
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean0.47340804
Minimum0
Maximum43301
Zeros7048907
Zeros (%)79.3%
Negative0
Negative (%)0.0%
Memory size67.8 MiB
2023-06-20T22:34:22.177512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum43301
Range43301
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.290586
Coefficient of variation (CV)44.973012
Kurtosis2698722.2
Mean0.47340804
Median Absolute Deviation (MAD)0
Skewness1557.8449
Sum4063622
Variance453.28904
MonotonicityNot monotonic
2023-06-20T22:34:22.358866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7048907
79.3%
1 848271
 
9.5%
2 298996
 
3.4%
3 130620
 
1.5%
4 73361
 
0.8%
5 43412
 
0.5%
6 30100
 
0.3%
7 19260
 
0.2%
8 14027
 
0.2%
9 10168
 
0.1%
Other values (5425) 66640
 
0.7%
(Missing) 302296
 
3.4%
ValueCountFrequency (%)
0 7048907
79.3%
0.018 1
 
< 0.1%
0.022 1
 
< 0.1%
0.024 1
 
< 0.1%
0.03 1
 
< 0.1%
0.032 1
 
< 0.1%
0.034 1
 
< 0.1%
0.038 1
 
< 0.1%
0.042 1
 
< 0.1%
0.044 2
 
< 0.1%
ValueCountFrequency (%)
43301 1
 
< 0.1%
27656 1
 
< 0.1%
27652 1
 
< 0.1%
13828 1
 
< 0.1%
13826 1
 
< 0.1%
6408 1
 
< 0.1%
1801 1
 
< 0.1%
1720 1
 
< 0.1%
1000 3
< 0.1%
816 1
 
< 0.1%

revenue
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct12155
Distinct (%)0.1%
Missing302296
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2.285173
Minimum0
Maximum84197.961
Zeros7049979
Zeros (%)79.3%
Negative0
Negative (%)0.0%
Memory size67.8 MiB
2023-06-20T22:34:22.554027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.76
Maximum84197.961
Range84197.961
Interquartile range (IQR)0

Descriptive statistics

Standard deviation54.06806
Coefficient of variation (CV)23.66038
Kurtosis966651.01
Mean2.285173
Median Absolute Deviation (MAD)0
Skewness815.45482
Sum19615381
Variance2923.3551
MonotonicityNot monotonic
2023-06-20T22:34:22.732971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7049979
79.3%
0.93 33675
 
0.4%
3.24 27591
 
0.3%
1.85 25154
 
0.3%
2.31 23568
 
0.3%
2.78 23341
 
0.3%
2.73 18024
 
0.2%
1.39 17439
 
0.2%
1.16 16214
 
0.2%
3.66 15914
 
0.2%
Other values (12145) 1332863
 
15.0%
(Missing) 302296
 
3.4%
ValueCountFrequency (%)
0 7049979
79.3%
0.01 158
 
< 0.1%
0.02 16
 
< 0.1%
0.03 10
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.1 1
 
< 0.1%
0.23 537
 
< 0.1%
0.25 1
 
< 0.1%
0.27 2
 
< 0.1%
ValueCountFrequency (%)
84197.961 1
< 0.1%
52496.852 1
< 0.1%
52488.699 1
< 0.1%
32490.51 1
< 0.1%
31150 1
< 0.1%
30327.01 1
< 0.1%
26711.859 1
< 0.1%
26247.59 1
< 0.1%
26243.801 1
< 0.1%
25423.73 1
< 0.1%

stock
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct9039
Distinct (%)0.1%
Missing302296
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean16.005747
Minimum0
Maximum4655
Zeros66086
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size67.8 MiB
2023-06-20T22:34:22.916121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q317
95-th percentile48
Maximum4655
Range4655
Interquartile range (IQR)13

Descriptive statistics

Standard deviation37.516921
Coefficient of variation (CV)2.3439656
Kurtosis1418.7495
Mean16.005747
Median Absolute Deviation (MAD)5
Skewness24.219273
Sum1.3738952 × 108
Variance1407.5194
MonotonicityNot monotonic
2023-06-20T22:34:23.090964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 619203
 
7.0%
3 617176
 
6.9%
6 600701
 
6.8%
2 585179
 
6.6%
5 571932
 
6.4%
1 464188
 
5.2%
7 435351
 
4.9%
8 387802
 
4.4%
9 354054
 
4.0%
12 353011
 
4.0%
Other values (9029) 3595165
40.5%
ValueCountFrequency (%)
0 66086
0.7%
0.001 38
 
< 0.1%
0.002 53
 
< 0.1%
0.003 65
 
< 0.1%
0.004 323
 
< 0.1%
0.005 333
 
< 0.1%
0.006 30
 
< 0.1%
0.007 15
 
< 0.1%
0.008 25
 
< 0.1%
0.009 11
 
< 0.1%
ValueCountFrequency (%)
4655 1
< 0.1%
4582 1
< 0.1%
4473 1
< 0.1%
4404 1
< 0.1%
4384 1
< 0.1%
4320 1
< 0.1%
4308 1
< 0.1%
4292 1
< 0.1%
4273 1
< 0.1%
4243 1
< 0.1%

price
Real number (ℝ)

Distinct606
Distinct (%)< 0.1%
Missing91381
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean15.753767
Minimum0.01
Maximum1599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.8 MiB
2023-06-20T22:34:23.270300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1
Q13.45
median8
Q316.95
95-th percentile53.9
Maximum1599
Range1598.99
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation32.77869
Coefficient of variation (CV)2.0806891
Kurtosis521.81016
Mean15.753767
Median Absolute Deviation (MAD)5.5
Skewness16.550523
Sum1.3854929 × 108
Variance1074.4425
MonotonicityNot monotonic
2023-06-20T22:34:23.452179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 243419
 
2.7%
3.95 156115
 
1.8%
3.5 147047
 
1.7%
0.75 146503
 
1.6%
2.95 137147
 
1.5%
19.9 133650
 
1.5%
11.9 131649
 
1.5%
1.75 116461
 
1.3%
12.9 115990
 
1.3%
2.5 109490
 
1.2%
Other values (596) 7357206
82.8%
ValueCountFrequency (%)
0.01 136
 
< 0.1%
0.25 7983
 
0.1%
0.3 107
 
< 0.1%
0.35 237
 
< 0.1%
0.4 1175
 
< 0.1%
0.45 12344
 
0.1%
0.5 45814
0.5%
0.58 512
 
< 0.1%
0.6 24658
0.3%
0.65 50459
0.6%
ValueCountFrequency (%)
1599 174
 
< 0.1%
1549 115
 
< 0.1%
1499 160
 
< 0.1%
1449 95
 
< 0.1%
1399 127
 
< 0.1%
1349 174
 
< 0.1%
849.9 574
< 0.1%
749.9 8
 
< 0.1%
749 63
 
< 0.1%
699.9 19
 
< 0.1%

promo_type_1
Categorical

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.8 MiB
PR14
7653515 
PR05
 
547253
PR10
 
213664
PR03
 
151863
PR06
 
124289
Other values (12)
 
195474

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35544232
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPR14
2nd rowPR14
3rd rowPR14
4th rowPR14
5th rowPR14

Common Values

ValueCountFrequency (%)
PR14 7653515
86.1%
PR05 547253
 
6.2%
PR10 213664
 
2.4%
PR03 151863
 
1.7%
PR06 124289
 
1.4%
PR07 57419
 
0.6%
PR12 40840
 
0.5%
PR09 35752
 
0.4%
PR17 32863
 
0.4%
PR01 12618
 
0.1%
Other values (7) 15982
 
0.2%

Length

2023-06-20T22:34:23.633127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr14 7653515
86.1%
pr05 547253
 
6.2%
pr10 213664
 
2.4%
pr03 151863
 
1.7%
pr06 124289
 
1.4%
pr07 57419
 
0.6%
pr12 40840
 
0.5%
pr09 35752
 
0.4%
pr17 32863
 
0.4%
pr01 12618
 
0.1%
Other values (7) 15982
 
0.2%

Most occurring characters

ValueCountFrequency (%)
P 8886058
25.0%
R 8886058
25.0%
1 7966930
22.4%
4 7656898
21.5%
0 1150417
 
3.2%
5 547272
 
1.5%
3 152470
 
0.4%
6 125201
 
0.4%
7 90282
 
0.3%
2 40840
 
0.1%
Other values (2) 41806
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17772116
50.0%
Decimal Number 17772116
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7966930
44.8%
4 7656898
43.1%
0 1150417
 
6.5%
5 547272
 
3.1%
3 152470
 
0.9%
6 125201
 
0.7%
7 90282
 
0.5%
2 40840
 
0.2%
9 35752
 
0.2%
8 6054
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 8886058
50.0%
R 8886058
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17772116
50.0%
Common 17772116
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7966930
44.8%
4 7656898
43.1%
0 1150417
 
6.5%
5 547272
 
3.1%
3 152470
 
0.9%
6 125201
 
0.7%
7 90282
 
0.5%
2 40840
 
0.2%
9 35752
 
0.2%
8 6054
 
< 0.1%
Latin
ValueCountFrequency (%)
P 8886058
50.0%
R 8886058
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35544232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 8886058
25.0%
R 8886058
25.0%
1 7966930
22.4%
4 7656898
21.5%
0 1150417
 
3.2%
5 547272
 
1.5%
3 152470
 
0.4%
6 125201
 
0.4%
7 90282
 
0.3%
2 40840
 
0.1%
Other values (2) 41806
 
0.1%

promo_bin_1
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing7653515
Missing (%)86.1%
Memory size67.8 MiB
verylow
514398 
low
259135 
moderate
193475 
high
146120 
veryhigh
119415 

Length

Max length8
Median length7
Mean length6.0572256
Min length3

Characters and Unicode

Total characters7465791
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowverylow
2nd rowmoderate
3rd rowlow
4th rowhigh
5th rowlow

Common Values

ValueCountFrequency (%)
verylow 514398
 
5.8%
low 259135
 
2.9%
moderate 193475
 
2.2%
high 146120
 
1.6%
veryhigh 119415
 
1.3%
(Missing) 7653515
86.1%

Length

2023-06-20T22:34:23.777226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T22:34:23.968672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
verylow 514398
41.7%
low 259135
21.0%
moderate 193475
 
15.7%
high 146120
 
11.9%
veryhigh 119415
 
9.7%

Most occurring characters

ValueCountFrequency (%)
e 1020763
13.7%
o 967008
13.0%
r 827288
11.1%
l 773533
10.4%
w 773533
10.4%
v 633813
8.5%
y 633813
8.5%
h 531070
7.1%
i 265535
 
3.6%
g 265535
 
3.6%
Other values (4) 773900
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7465791
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1020763
13.7%
o 967008
13.0%
r 827288
11.1%
l 773533
10.4%
w 773533
10.4%
v 633813
8.5%
y 633813
8.5%
h 531070
7.1%
i 265535
 
3.6%
g 265535
 
3.6%
Other values (4) 773900
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7465791
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1020763
13.7%
o 967008
13.0%
r 827288
11.1%
l 773533
10.4%
w 773533
10.4%
v 633813
8.5%
y 633813
8.5%
h 531070
7.1%
i 265535
 
3.6%
g 265535
 
3.6%
Other values (4) 773900
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7465791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1020763
13.7%
o 967008
13.0%
r 827288
11.1%
l 773533
10.4%
w 773533
10.4%
v 633813
8.5%
y 633813
8.5%
h 531070
7.1%
i 265535
 
3.6%
g 265535
 
3.6%
Other values (4) 773900
10.4%

promo_type_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.8 MiB
PR03
8873337 
PR02
 
7026
PR04
 
2892
PR01
 
2803

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35544232
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPR03
2nd rowPR03
3rd rowPR03
4th rowPR03
5th rowPR03

Common Values

ValueCountFrequency (%)
PR03 8873337
99.9%
PR02 7026
 
0.1%
PR04 2892
 
< 0.1%
PR01 2803
 
< 0.1%

Length

2023-06-20T22:34:24.138895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T22:34:24.310277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pr03 8873337
99.9%
pr02 7026
 
0.1%
pr04 2892
 
< 0.1%
pr01 2803
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P 8886058
25.0%
R 8886058
25.0%
0 8886058
25.0%
3 8873337
25.0%
2 7026
 
< 0.1%
4 2892
 
< 0.1%
1 2803
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17772116
50.0%
Decimal Number 17772116
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8886058
50.0%
3 8873337
49.9%
2 7026
 
< 0.1%
4 2892
 
< 0.1%
1 2803
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 8886058
50.0%
R 8886058
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17772116
50.0%
Common 17772116
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8886058
50.0%
3 8873337
49.9%
2 7026
 
< 0.1%
4 2892
 
< 0.1%
1 2803
 
< 0.1%
Latin
ValueCountFrequency (%)
P 8886058
50.0%
R 8886058
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35544232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 8886058
25.0%
R 8886058
25.0%
0 8886058
25.0%
3 8873337
25.0%
2 7026
 
< 0.1%
4 2892
 
< 0.1%
1 2803
 
< 0.1%

promo_bin_2
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing8873337
Missing (%)99.9%
Memory size67.8 MiB
verylow
6441 
high
3637 
veryhigh
2643 

Length

Max length8
Median length7
Mean length6.3500511
Min length4

Characters and Unicode

Total characters80779
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowverylow
2nd rowverylow
3rd rowverylow
4th rowverylow
5th rowverylow

Common Values

ValueCountFrequency (%)
verylow 6441
 
0.1%
high 3637
 
< 0.1%
veryhigh 2643
 
< 0.1%
(Missing) 8873337
99.9%

Length

2023-06-20T22:34:24.468233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T22:34:24.651392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
verylow 6441
50.6%
high 3637
28.6%
veryhigh 2643
20.8%

Most occurring characters

ValueCountFrequency (%)
h 12560
15.5%
v 9084
11.2%
e 9084
11.2%
r 9084
11.2%
y 9084
11.2%
l 6441
8.0%
o 6441
8.0%
w 6441
8.0%
i 6280
7.8%
g 6280
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80779
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 12560
15.5%
v 9084
11.2%
e 9084
11.2%
r 9084
11.2%
y 9084
11.2%
l 6441
8.0%
o 6441
8.0%
w 6441
8.0%
i 6280
7.8%
g 6280
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 80779
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 12560
15.5%
v 9084
11.2%
e 9084
11.2%
r 9084
11.2%
y 9084
11.2%
l 6441
8.0%
o 6441
8.0%
w 6441
8.0%
i 6280
7.8%
g 6280
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 12560
15.5%
v 9084
11.2%
e 9084
11.2%
r 9084
11.2%
y 9084
11.2%
l 6441
8.0%
o 6441
8.0%
w 6441
8.0%
i 6280
7.8%
g 6280
7.8%

promo_discount_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing8873337
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean30.110605
Minimum16
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.8 MiB
2023-06-20T22:34:24.784515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile20
Q120
median20
Q335
95-th percentile50
Maximum50
Range34
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.8509
Coefficient of variation (CV)0.39357893
Kurtosis-1.0464257
Mean30.110605
Median Absolute Deviation (MAD)4
Skewness0.66762654
Sum383037
Variance140.44382
MonotonicityNot monotonic
2023-06-20T22:34:24.929722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20 6226
 
0.1%
33 2804
 
< 0.1%
50 2643
 
< 0.1%
35 585
 
< 0.1%
40 248
 
< 0.1%
16 215
 
< 0.1%
(Missing) 8873337
99.9%
ValueCountFrequency (%)
16 215
 
< 0.1%
20 6226
0.1%
33 2804
< 0.1%
35 585
 
< 0.1%
40 248
 
< 0.1%
50 2643
< 0.1%
ValueCountFrequency (%)
50 2643
< 0.1%
40 248
 
< 0.1%
35 585
 
< 0.1%
33 2804
< 0.1%
20 6226
0.1%
16 215
 
< 0.1%

promo_discount_type_2
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing8873337
Missing (%)99.9%
Memory size67.8 MiB
PR01
3762 
PR02
3648 
PR04
2793 
PR03
2518 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters50884
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPR04
2nd rowPR02
3rd rowPR04
4th rowPR02
5th rowPR02

Common Values

ValueCountFrequency (%)
PR01 3762
 
< 0.1%
PR02 3648
 
< 0.1%
PR04 2793
 
< 0.1%
PR03 2518
 
< 0.1%
(Missing) 8873337
99.9%

Length

2023-06-20T22:34:25.091346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-20T22:34:25.265481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pr01 3762
29.6%
pr02 3648
28.7%
pr04 2793
22.0%
pr03 2518
19.8%

Most occurring characters

ValueCountFrequency (%)
P 12721
25.0%
R 12721
25.0%
0 12721
25.0%
1 3762
 
7.4%
2 3648
 
7.2%
4 2793
 
5.5%
3 2518
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25442
50.0%
Decimal Number 25442
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12721
50.0%
1 3762
 
14.8%
2 3648
 
14.3%
4 2793
 
11.0%
3 2518
 
9.9%
Uppercase Letter
ValueCountFrequency (%)
P 12721
50.0%
R 12721
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25442
50.0%
Common 25442
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12721
50.0%
1 3762
 
14.8%
2 3648
 
14.3%
4 2793
 
11.0%
3 2518
 
9.9%
Latin
ValueCountFrequency (%)
P 12721
50.0%
R 12721
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 12721
25.0%
R 12721
25.0%
0 12721
25.0%
1 3762
 
7.4%
2 3648
 
7.2%
4 2793
 
5.5%
3 2518
 
4.9%

Interactions

2023-06-20T22:33:05.914025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:25.473212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:34.273882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:42.463560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:50.632759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:59.071976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:06.190600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:27.230212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:35.815640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:44.018018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:52.230864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:00.656211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:06.460824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:28.965567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:37.404364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:45.566397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:53.933558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:02.204046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:06.746926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:30.545591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:38.986812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:47.130280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:55.458511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:03.791449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:07.055185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:32.238459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:40.566964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:48.678071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:57.037570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:05.315363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:07.300601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:32.543891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:40.870332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:48.980558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:32:57.329136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-20T22:33:05.624028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-06-20T22:34:25.415093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unnamed: 0salesrevenuestockpricepromo_discount_2store_idpromo_type_1promo_bin_1promo_type_2promo_bin_2promo_discount_type_2
Unnamed: 01.000-0.039-0.039-0.0110.0250.1500.9470.0130.0210.0310.4120.309
sales-0.0391.0000.9920.202-0.2520.0540.0000.0001.0000.0001.0001.000
revenue-0.0390.9921.0000.184-0.2080.0530.0010.0070.0030.0001.0001.000
stock-0.0110.2020.1841.000-0.3600.0310.0180.0040.0060.0001.0001.000
price0.025-0.252-0.208-0.3601.000-0.1380.0280.0640.0430.0020.0780.100
promo_discount_20.1500.0540.0530.031-0.1381.0000.0960.3480.7710.9851.0000.777
store_id0.9470.0000.0010.0180.0280.0961.0000.0250.0740.0080.1010.162
promo_type_10.0130.0000.0070.0040.0640.3480.0251.0000.4300.0130.3030.265
promo_bin_10.0211.0000.0030.0060.0430.7710.0740.4301.0000.0290.8720.832
promo_type_20.0310.0000.0000.0000.0020.9850.0080.0130.0291.0000.8850.896
promo_bin_20.4121.0001.0001.0000.0781.0000.1010.3030.8720.8851.0000.904
promo_discount_type_20.3091.0001.0001.0000.1000.7770.1620.2650.8320.8960.9041.000

Missing values

2023-06-20T22:33:15.695607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-20T22:33:30.316003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-20T22:34:08.806081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0store_idproduct_iddatesalesrevenuestockpricepromo_type_1promo_bin_1promo_type_2promo_bin_2promo_discount_2promo_discount_type_2
01S0002P00012017-01-020.00.008.06.25PR14NaNPR03NaNNaNNaN
12S0002P00052017-01-020.00.0011.033.90PR14NaNPR03NaNNaNNaN
23S0002P00112017-01-020.00.009.049.90PR14NaNPR03NaNNaNNaN
34S0002P00152017-01-021.02.4119.02.60PR14NaNPR03NaNNaNNaN
45S0002P00172017-01-020.00.0012.01.49PR14NaNPR03NaNNaNNaN
56S0002P00182017-01-021.01.8137.01.95PR14NaNPR03NaNNaNNaN
67S0002P00242017-01-020.00.0036.01.95PR14NaNPR03NaNNaNNaN
78S0002P00352017-01-022.04.5415.02.45PR14NaNPR03NaNNaNNaN
89S0002P00462017-01-020.00.0011.034.50PR14NaNPR03NaNNaNNaN
910S0002P00512017-01-027.04.54132.00.70PR14NaNPR03NaNNaNNaN
Unnamed: 0store_idproduct_iddatesalesrevenuestockpricepromo_type_1promo_bin_1promo_type_2promo_bin_2promo_discount_2promo_discount_type_2
88860488886049S0143P06392019-10-31NaNNaNNaN9.75PR14NaNPR03NaNNaNNaN
88860498886050S0143P06422019-10-31NaNNaNNaN4.00PR14NaNPR03NaNNaNNaN
88860508886051S0143P06582019-10-31NaNNaNNaN41.50PR14NaNPR03NaNNaNNaN
88860518886052S0143P06632019-10-31NaNNaNNaN6.75PR10verylowPR03NaNNaNNaN
88860528886053S0143P06642019-10-31NaNNaNNaN1.75PR14NaNPR03NaNNaNNaN
88860538886054S0143P06762019-10-31NaNNaNNaN19.90PR03verylowPR03NaNNaNNaN
88860548886055S0143P06802019-10-31NaNNaNNaN139.90PR14NaNPR03NaNNaNNaN
88860558886056S0143P06942019-10-31NaNNaNNaN7.50PR14NaNPR03NaNNaNNaN
88860568886057S0143P07182019-10-31NaNNaNNaN23.75PR14NaNPR03NaNNaNNaN
88860578886058S0143P07472019-10-31NaNNaNNaN21.90PR14NaNPR03NaNNaNNaN